{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "def getGauseDistribution(idx, k):\n",
    "    ans = np.random.normal(loc = idx - k / 2, scale = 1)\n",
    "    return ans\n",
    "\n",
    "def K_Bet(k = 10, step = 2000, sigma = 0.1, positive = 0):\n",
    "    sumR = 0\n",
    "    sumContainer = [sumR]\n",
    "    Q = [positive for i in range(k)]\n",
    "    N = [positive for i in range(k)]\n",
    "    for i in range(step):\n",
    "        tmp = np.random.uniform(0, 1)\n",
    "        if tmp <= 1 - sigma:\n",
    "            idx = np.argmax(np.array(Q))\n",
    "        else:\n",
    "            idx = np.random.randint(0, k)\n",
    "        r = getGauseDistribution(idx, k)\n",
    "        sumR = sumR + r\n",
    "        avg = sumR / (i + 1)\n",
    "        sumContainer.append(avg)\n",
    "        N[idx] += 1\n",
    "        Q[idx] = Q[idx] + (r - Q[idx]) / N[idx]\n",
    "    return sumContainer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28d84a4bc08>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 10\n",
    "sigma = 0.1\n",
    "n_step = 1000\n",
    "attemp0 = K_Bet(k = k, step = n_step, sigma = sigma)\n",
    "attemp1 = K_Bet(k, n_step, 0.0)\n",
    "attemp2 = K_Bet(k, n_step, 0.01)\n",
    "attemp3 = K_Bet(k, n_step, 0.0, 10)\n",
    "# attemp0[1000]\n",
    "plt.plot(attemp0, \"r\", label = 'epis=0.1')\n",
    "plt.plot(attemp1, \"g\", label = 'epis=0.0')\n",
    "plt.plot(attemp2, \"b\", label = 'epis=0.01')\n",
    "plt.plot(attemp3, \"y\", label = \"positive=20, epis=0.0\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def randomWalk(x):\n",
    "    x_dim = np.size(x)\n",
    "    choices = [-1, 0, 1]\n",
    "    for i in range(x_dim):\n",
    "        x[i] = x[i] + np.random.choice(choices)\n",
    "    return x\n",
    "\n",
    "def epislonGreedy(x, epsilon):\n",
    "    tmp = np.random.uniform(0, 1)\n",
    "    if tmp <= 1- epsilon:\n",
    "        idx = np.argmax(x)\n",
    "    else:\n",
    "        idx = np.random.randint(0, np.size(x))\n",
    "    return idx\n",
    "\n",
    "def K_Bet_Unstable(step, task_number, epsilon, arm_number, lr, c = 2):\n",
    "\n",
    "    Q = np.zeros(shape = (task_number, arm_number)) \n",
    "    N = np.zeros(shape = (task_number, arm_number))\n",
    "    R = np.zeros(step)\n",
    "\n",
    "    realQ = np.zeros(shape = (task_number, arm_number))\n",
    "    \n",
    "    R_confidence = np.zeros(step)\n",
    "    Q_confidence = np.zeros(shape = (task_number, arm_number))\n",
    "    N_confidence = np.zeros(shape = (task_number, arm_number))\n",
    "\n",
    "    for i in range(step):\n",
    "        for j in range(task_number):\n",
    "            realq = realQ[j, :]\n",
    "            realq = randomWalk(realq)\n",
    "            realQ[j, :] = realq\n",
    "            q = Q[j, :]\n",
    "            idx = epislonGreedy(q, epsilon)\n",
    "            N[j][idx] += 1\n",
    "            reward = realq[idx]\n",
    "            q[idx] = q[idx] + lr * (reward - q[idx])\n",
    "            Q[j, :] = q\n",
    "            R[i] = R[i] + reward\n",
    "        R[i] = R[i] / task_number\n",
    "    return R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "step = 1000\n",
    "task_number = 500\n",
    "epsilon = 0.1\n",
    "arm_number = 10\n",
    "lr = 0.1\n",
    "R1 = K_Bet_Unstable(step, task_number, epsilon, arm_number, lr)\n",
    "R2 = K_Bet_Unstable(step, task_number, epsilon, arm_number, 0.3)\n",
    "plt.xlabel('Steps')\n",
    "plt.ylabel('Average Reward')\n",
    "plt.plot(R1, color = 'b', label = \"lr=0.1\")\n",
    "plt.plot(R2, color = 'r', label = \"lr=0.3\")\n",
    "plt.xticks(np.arange(0, step+1, 100))\n",
    "plt.legend()\n",
    "plt.savefig(\"unstableProcess.png\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "494899efd6527d56ea7f55c588d0081523a17dc3a9ff1107f3394ad815ff2527"
  },
  "kernelspec": {
   "display_name": "Python 3.7.7 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
